Li Jinman, Lin Yang, An Chuangfeng, Li Xiang, Xiao Tianhao, Hou Luyao, Qi Jianing, Gong Jing
Tianjin Branch of CNOOC (China) Co., Ltd., Tianjin 300452, China.
China University of Petroleum-Beijing, Changping, Beijing 102249, China.
ACS Omega. 2024 Dec 18;9(52):51570-51579. doi: 10.1021/acsomega.4c09098. eCollection 2024 Dec 31.
One of the key points in the construction of smart oil and gas fields is the effective utilization of data. Virtual Flow Metering (VFM), as one of the representative research directions for digital transformation, can obtain real-time production from oil and gas wells without the need for additional field instrumentation, utilizing pressure and temperature data obtained from sensors and employing multiphase flow mechanism models. The data-driven VFM demonstrates a commendable capacity in capturing the nonlinear relationship between sensor data and flow rates, while circumventing the necessity for rigorous analysis of the underlying mechanistic processes. However, this approach also faces the problem of poor model interpretability and uncertainty in the reliability of the output results. To enhance the reliability of data-driven models, this study proposes a hybrid model that integrates knowledge into the data-driven model. We added a constraint containing prior knowledge to the Long Short-Term Memory neural network to guide data-driven model training and established a Knowledge-Guided Predictive Model (KGPM) suitable for VFM. Through a series of comparative experimental analyses, our proposed model has demonstrated exceptional proficiency in flow rate prediction, with a Mean Absolute Percentage Error of 3.211% and 1.141% for the two experimental wells. This research contributes to the optimization of VFM techniques, making a significant contribution to the efficient construction of intelligent oil and gas fields.
智能油气田建设的关键要点之一是数据的有效利用。虚拟流量计量（VFM）作为数字转型的代表性研究方向之一，无需额外的现场仪表，利用从传感器获取的压力和温度数据，并采用多相流机理模型，就能从油气井中获取实时产量。数据驱动的VFM在捕捉传感器数据与流量之间的非线性关系方面表现出值得称赞的能力，同时避免了对潜在机理过程进行严格分析的必要性。然而，这种方法也面临模型可解释性差以及输出结果可靠性存在不确定性的问题。为提高数据驱动模型的可靠性，本研究提出了一种将知识集成到数据驱动模型中的混合模型。我们在长短期记忆神经网络中添加了一个包含先验知识的约束来指导数据驱动模型训练，并建立了适用于VFM的知识引导预测模型（KGPM）。通过一系列对比实验分析，我们提出的模型在流量预测方面表现出卓越的能力，两口实验井的平均绝对百分比误差分别为3.211%和1.141%。本研究有助于优化VFM技术，为智能油气田的高效建设做出了重大贡献。
